Artificial Intelligence Master Analyst
Future-proof yourself with AI & ML course designed and delivered by IIT Faculty and IBM professionals.

The primary method of instruction is virtual instructor-led training lectures by eminent faculty & industry professionals. These Live-Tutored sessions and not recorded sessions will be beamed online via internet to participant’s desktop/ laptop or classrooms. All enrolled participant’s will also, be provided access to other learning aids, reference materials, assessments, and hands-on workshops as appropriate. Throughout the duration of the course, students will have the flexibility to reach out to the faculty, real-time during the LIVE lectures or offline to raise questions and clear their queries.
At the end of the course, students will also be allocated Project work that is designed to provide adequate practical and hands-on experience in implementing the concepts learned during the course

Duration: 96 Hours Session: 2 hours/day - 2 days a week Batch Size: 50 Students

Affordable Price

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Instant Certification

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This IIT & IBM Certified course is focused on building industry-ready “Artificial Intelligence Analyst” & “Data Scientist” who can work on machine learning, data mining, and statistical modelling for predictive and prescriptive enterprise analytics. This program will enable you to develop deep understanding and experience with machine learning and data analysis. Familiarity with common tools for data management and analysis including machine learning can be applied on real world problems for building predictive models using machine learning on your own.

Syllabus Structure

What you will learn from this course

Module 1 : Introduction to AI
  • Rise of Artificial Intelligence
Module 2 : Python Programming Basics
  • Introduction to Python Programming
  • Packages in Python
  • Data Types and Operations
  • Statements and Syntax in Python
  • File Operations
  • Database Connection
  • Industry Talk
Module 3 : Data, Knowledge and Machine Learning with Python
  • Introduction to Machine Learning & Artificial Intelligence
  • Building Intelligent Machines to Transform Data into Knowledge
  • Applications of AI
  • Data Pre-processing, Dealing with Missing Data, Handling Categorical Data, Data Normalization
  • Data Visualization
  • Probability Theory, Probabilistic Learning in Python, Probability Distribution
  • The Naive Bayes Algorithm, Industry Talk, and Statistical Analysis
  • Unsupervised Learning, Hierarchical Clustering
  • Self Organizing Maps, K means Clustering
  • Practical Learning
  • Supervised Learning & Classification
  • Naïve Bayes Classifier
  • Regression Analysis
  • K Nearest Neighbour Algorithm
  • Support Vector Machine
  • Artificial Neural Network
  • Reinforcement Learning, Exercise Using Python
  • Deep Learning in Python, Convolutional Neural Network
  • Multilayer Neural Network, Industry Talk
Module 4 : Artificial Intelligence with Python
  • Logic Programming
  • Heuristic Search
  • Building Games with AI
  • Industry Talk

Module 5 : IBM Watson
  • Introduction to IBM Watson
  • Evolution from Deepqa to Watson Services on IBM cloud
  • Build with Watson
  • Exercise 1. Exploring Watson Services with Watson API Explore
Module 6 : Natural Language Processing
  • Introduction to Natural Language Processing
  • Pipeline and Concepts
  • NLP and IBM Watson
  • Exercise 2. Ingest, Convert, Enrich, and Query with Watson Discovery Service
Module 7 : Chatbots
  • Introduction to Chatbots
  • Chatbot Fundamentals
  • IBM Watson Conversation Service
  • Exercise 3. Getting Started with Watson Conversation Services
Module 8 : Computer Vision
  • Introduction to Computer Vision
  • Computer Vision Fundamentals
  • IBM Watson Visual Recognition Service
  • Exercise 4. Classifying Images with Watson Visual Recognition

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